# RocAlphaGo

**Repository Path**: mirrors_jfinal/RocAlphaGo

## Basic Information

- **Project Name**: RocAlphaGo
- **Description**: An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search" (Nature 529, 484-489, 28 Jan 2016), details of which can be found on their website https://deepmind.com/publications.html.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: develop
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-25
- **Last Updated**: 2025-11-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# RocAlphaGo

(Previously known just as "AlphaGo," renamed to clarify that we are not affiliated with DeepMind)

This project is a student-led replication/reference implementation of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search," details of which can be found [on their website](http://deepmind.com/alpha-go.html). This implementation uses Python and Keras - a decision to prioritize code clarity, at least in the early stages.

[![Build Status](https://travis-ci.org/Rochester-NRT/RocAlphaGo.svg?branch=develop)](https://travis-ci.org/Rochester-NRT/RocAlphaGo)
[![Gitter](https://badges.gitter.im/Rochester-NRT/RocAlphaGo.svg)](https://gitter.im/Rochester-NRT/RocAlphaGo?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)

# Documentation

See the [project wiki](https://github.com/Rochester-NRT/RocAlphaGo/wiki).

# Current project status

_This is not yet a full implementation of AlphaGo_. Development is being carried out on the `develop` branch. The current emphasis is on speed optimizations, which are necessary to complete training of the value-network and to create feasible tree-search. See the `cython-optimization` branch for more.

Selected data (i.e. trained models) are released in our [data repository](http://github.com/Rochester-NRT/RocAlphaGo.data).

This project has primarily focused on the neural network training aspect of DeepMind's AlphaGo. We also have a simple single-threaded implementation of their tree search algorithm, though it is not fast enough to be competitive yet.

See the wiki page on the [training pipeline](https://github.com/Rochester-NRT/RocAlphaGo/wiki/04.-Neural-Networks-and-Training) for information on how to run the training commands.

# How to contribute

See the ['Contributing'](CONTRIBUTING.md) document and join the [Gitter chat](https://gitter.im/Rochester-NRT/RocAlphaGo).